Dataset Information

Accuracy of magnetic resonance imaging for the diagnosis of multiple sclerosis: systematic review.

ABSTRACT: To determine the accuracy of magnetic resonance imaging criteria for the early diagnosis of multiple sclerosis in patients with suspected disease.Systematic review.12 electronic databases, citation searches, and reference lists of included studies. Review methods Studies on accuracy of diagnosis that compared magnetic resonance imaging, or diagnostic criteria incorporating such imaging, to a reference standard for the diagnosis of multiple sclerosis.29 studies (18 cohort studies, 11 other designs) were included. On average, studies of other designs (mainly diagnostic case-control studies) produced higher estimated diagnostic odds ratios than did cohort studies. Among 15 studies of higher methodological quality (cohort design, clinical follow-up as reference standard), those with longer follow-up produced higher estimates of specificity and lower estimates of sensitivity. Only two such studies followed patients for more than 10 years. Even in the presence of many lesions (> 10 or > 8), magnetic resonance imaging could not accurately rule multiple sclerosis in (likelihood ratio of a positive test result 3.0 and 2.0, respectively). Similarly, the absence of lesions was of limited utility in ruling out a diagnosis of multiple sclerosis (likelihood ratio of a negative test result 0.1 and 0.5).Many evaluations of the accuracy of magnetic resonance imaging for the early detection of multiple sclerosis have produced inflated estimates of test performance owing to methodological weaknesses. Use of magnetic resonance imaging to confirm multiple sclerosis on the basis of a single attack of neurological dysfunction may lead to over-diagnosis and over-treatment.

SUBMITTER: Whiting P

PROVIDER: S-EPMC1479850 | biostudies-other | 2006 Apr

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Accuracy of magnetic resonance imaging for the diagnosis of multiple sclerosis: systematic review.

Whiting Penny P Harbord Roger R Main Caroline C Deeks Jonathan J JJ Filippini Graziella G Egger Matthias M Sterne Jonathan A C JA

BMJ (Clinical research ed.) 20060324 7546

<h4>Objective</h4>To determine the accuracy of magnetic resonance imaging criteria for the early diagnosis of multiple sclerosis in patients with suspected disease.<h4>Design</h4>Systematic review.<h4>Data sources</h4>12 electronic databases, citation searches, and reference lists of included studies. Review methods Studies on accuracy of diagnosis that compared magnetic resonance imaging, or diagnostic criteria incorporating such imaging, to a reference standard for the diagnosis of multiple sc ...[more]

PMID: 16565096

Similar Datasets

Project description:IntroductionIntestinal endometriosis is considered the most severe form of deep endometriosis, the rectosigmoid being involved in about 90% of cases of bowel infiltration. Transvaginal sonography (TVS) and magnetic resonance imaging (MRI) have been used for noninvasive diagnosis and preoperative mapping of rectosigmoid endometriosis (RE), but no consensus has been reached so far regarding which method is the most accurate in this setting.ObjectiveWe aimed at performing a systematic review and meta-analysis to compare the accuracy of TVS versus MRI in the diagnosis of RE in a same population.MethodsA systematic review was conducted in accordance with the PRISMA guidelines. Studies were identified by searching the MEDLINE, Embase, and LILACS databases, as well the reference lists of retrieved articles, through February 2019. We included all cross-sectional studies that evaluated the accuracy of TVS versus MRI in the diagnosis of RE within a same sample of subjects and that used surgical findings with histological confirmation as the gold standard. The QUADAS-2 instrument was used to evaluate study quality. Sensitivity, specificity, positive likelihood ratios (LR+), and negative likelihood ratios (LR-) for the diagnosis of RE were calculated. This study is registered with PROSPERO, number CRD42017064378.ResultsEight studies (n = 1132) were included in the meta-analysis. The pooled sensitivity, specificity, LR+, and LR- values of MRI for RE were 90% (95% CI, 87-92%), 96% (95% CI, 94-97%), 17.26 (95% CI, 3.57-83.50), and 0.15 (95% CI, 0.10-0.23); values of TVS were 90% [95% CI, 87-92%], 96% (95% CI, 94-97%), 20.66 (95% CI, 8.71-49.00) and 0.12 (95% CI, 0.08-0.20), respectively. Areas under the S-ROC curves (AUC) showed no statistically significant differences between MRI (AUC = 0.948) and TVS (AUC = 0.930) in the diagnosis of RE (P = 0.13). Moreover, considering the average prevalence among the studies of 47.3%, both methods demonstrated similarly high positive post-test probabilities (93.9% for TVS and 94.8% for MRI), and the combined use of them yielded a post-test probability of 99.6%.ConclusionMRI and TVS have similarly high accuracy and positive post-test probabilities in the noninvasive diagnosis of RE. Combination of MRI and TVS may increase even further the positive post-test probabilities to near 100%.

Project description:BackgroundIn the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy.ObjectiveThis study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD.MethodsThe following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC).ResultsA total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67.ConclusionsThe accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings.Trial registrationPROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779.

Project description:Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS). Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability. Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression. Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Dataset Information

Accuracy of magnetic resonance imaging for the diagnosis of multiple sclerosis: systematic review.

Publications

Accuracy of magnetic resonance imaging for the diagnosis of multiple sclerosis: systematic review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure